Case-Based Reasoning: Experiences, Lessons, and Future Directions - Softcover

Softcover

ISBN 10: 026262110X ISBN 13: 9780262621106

Publisher: MIT Press, 1996

Synopsis

Case-based reasoning (CBR) is a flourishing paradigm for reasoning and learning in artificial intelligence, with major research efforts and burgeoning applications extending the frontiers of the field. This book provides an introduction for students as well as an up-to-date overview for experienced researchers and practitioners. It examines the field in a "case-based" way, through concrete examples of how key issues -- including indexing and retrieval, case adaptation, evaluation, and application of CBR methods -- are being addressed in the context of a range of tasks and domains. Complementing these case studies are commentaries by leading researchers on the lessons learned from experiences with CBR and visions for the roles in which case-based reasoning can have the greatest impact. A tutorial introduction by Janet Kolodner, one of the originators of CBR, and David Leake makes the book accessible to students and developers starting to apply case-based reasoning. The volume can also serve as a suitable companion for a CBR or introductory AI textbook.

About this title

From the Back Cover

This book presents a selection of recent progress, issues, and directions for the future of case-based reasoning. It includes chapters addressing fundamental issues and approaches in indexing and retrieval, situation assessment and similarity assessment, and in case adaptation. Those chapters provide a "case-based" view of key problems and solutions in context of the tasks for which they were developed. It also presents lessons learned about how to design CBR systems and how to apply them to real-world problems. The final chapters include a perspective on the state of the field and the most important directions for future impact. The case studies presented involve a broad sampling of tasks, such as design, education, legal reasoning, planning, decision support, problem-solving, and knowledge navigation. In addition, they experimentally examine one of the fundamental tenets of CBR, that reasoning from prior experiences improves performance. The chapters also address other issues that, while not restricted to CBR per se, have been vigorously attacked by the CBR community, including creative problem-solving, strategic memory search, and opportunistic retrieval. This volume provides a vision of the present, and a challenge for the future, of case-based reasoning research and applications.

Synopsis

Case-based reasoning (CBR) is a paradigm for reasoning and learning in artificial intelligence, with research efforts and applications extending the frontiers of the field. This book provides an introduction for students as well as an up-to-date overview for experienced researchers and practitioners. It examines the field in a "case-based" way, through concrete examples of how key issues - including indexing and retrieval, case adaptation, evaluation and application of CBR methods - are being addressed in the context of a range of tasks and domains. Complementing these case studies are commentaries by leading researchers on the lessons learned from experiences with CBR and visions for the roles in which case-based reasoning can have the greatest impact. A tutorial introduction by Janet Kolodner, one of the originators of CBR, and David Leake should make the book accessible to students and developers starting to apply case-based reasoning. The volume can also serve as a companion for a CBR or introductory AI textbook.

Edition details

• Publisher: MIT Press
• Publication date: 1996
• Language: English
• ISBN 10: 026262110X
• ISBN 13: 9780262621106
• Binding: Paperback
• Number of pages: 438
• Editor: Leake David